Abstract-One of the modified versions of the PID controller is the I-PD controller. It was proposed for eliminating the proportional and derivative kick appeared during set point change. In this paper, the optimal I-PD controller design for DC motor speed control system by the adaptive tabu search (ATS), one of the most efficient metaheuristic optimization techniques, is proposed. In a control system, DC motor is the principle and it is widely used because of the power from existing direct-current lighting power distribution systems. It can be controlled over a wide range and a variable supply. In this research, TMS320F28335 DSP microcontroller is implemented for DC motor speed control. This processor consists of the several peripheral circuits for motor drive application such as analog to digital, encoder digital to analog and PWM input/output interface circuits. These interface circuits can be used in both of DC and AC motor controls. For the control algorithm development and the Code Composer Studio (CCS) compiler can be used together with TMS320F28335 DSP in MATLAB/SIMULINK. This proposed method is tested with the DC motor, 1260, 1400, 1540 rpm and 24 volts, consecutively to verify the performance of the I-PD controller designed for DC motor speed control system using the speed and control signal response to many load disturbances. The simulation of DC motor is based on MATLAB/SIMULINK. The implementation results are compared with the simulation results. The correlation in the experiment shows that they are high related. In this paper show the effectiveness of the proposed methods and discuss how they could generalize to other systems by the simulation and experimentation. The results show that the I-PD parameters can be optimized by the ATS. The controlled system with I-PD provides better responses once compared to that with a basic parallel PID controller.
This paper proposes the artificial intelligence (AI)-based optimal PID controller design optimization of brushless direct current (BLDC) motor speed control with phase advance approach. The proposed control system allows the speed adjustment of the BLDC motor by phase advance technique. In this paper, two selected AI algorithms, i.e., the adaptive tabu search (ATS) and the intensified current search (ICS) are conducted as the optimizer for the PID controller design. The proposed control system is simulated by MATLAB/SIMULINK. Results obtained by the ATS and ICS will be compared with those obtained by the Ziegler-Nichols (ZN) tuning rule and the genetic algorithm (GA). It shows that the speed response of the BLDC motor by phase advance with the PID controller optimized by the ICS outperforms better than the ZN, GA and ATS.
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